JP2019220193A - System and method related to mobile advertisement supply on marketing - Google Patents

System and method related to mobile advertisement supply on marketing Download PDF

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Publication number
JP2019220193A
JP2019220193A JP2019131465A JP2019131465A JP2019220193A JP 2019220193 A JP2019220193 A JP 2019220193A JP 2019131465 A JP2019131465 A JP 2019131465A JP 2019131465 A JP2019131465 A JP 2019131465A JP 2019220193 A JP2019220193 A JP 2019220193A
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Japan
Prior art keywords
location
request
advertisement
mobile
database
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JP2019131465A
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Japanese (ja)
Inventor
フイタオ ルオ
Huitao Luo
フイタオ ルオ
ニシャント カトリ
Nishant Khatri
ニシャント カトリ
プラカッシュ ムッティネニ
Muttineni Prakash
プラカッシュ ムッティネニ
スリハリ ヴェンカテサン
Srihari Venkatesan
スリハリ ヴェンカテサン
ディパンシュ シャマ
Shama Dipanshu
ディパンシュ シャマ
スティーブン アンダーソン
Stephen Anderson
スティーブン アンダーソン
ジョージ ルコウト
Rekouts George
ジョージ ルコウト
ジョナサン シュヴァルツ
Jonathan Schwartz
ジョナサン シュヴァルツ
デービット チョック
Chock David
デービット チョック
シャンシャン ツオ
Shanshan Tuo
シャンシャン ツオ
キャン リアン
Can Liang
キャン リアン
Original Assignee
エックスアド インコーポレーテッドXad,Inc.
Xad Inc
エックスアド インコーポレーテッドXad,Inc.
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Priority to US62/000,501 priority Critical
Priority to US201462000497P priority
Priority to US201462000499P priority
Priority to US201462000494P priority
Priority to US201462000496P priority
Priority to US201462000501P priority
Priority to US62/000,496 priority
Priority to US62/000,499 priority
Priority to US62/000,497 priority
Priority to US62/000,494 priority
Priority to US62/013,527 priority
Priority to US201462013527P priority
Priority to US62/066,912 priority
Priority to US201462066912P priority
Priority to US62/067,965 priority
Priority to US201462067965P priority
Priority to US62/119,807 priority
Priority to US201562119807P priority
Application filed by エックスアド インコーポレーテッドXad,Inc., Xad Inc, エックスアド インコーポレーテッドXad,Inc. filed Critical エックスアド インコーポレーテッドXad,Inc.
Publication of JP2019220193A publication Critical patent/JP2019220193A/en
Pending legal-status Critical Current

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Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W4/00Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/20Services signaling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel
    • H04W4/21Services signaling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel for social networking applications
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29Geographical information databases
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0241Advertisement
    • G06Q30/0251Targeted advertisement
    • G06Q30/0261Targeted advertisement based on user location
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0241Advertisement
    • G06Q30/0251Targeted advertisement
    • G06Q30/0267Wireless devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0241Advertisement
    • G06Q30/0273Fees for advertisement
    • G06Q30/0275Auctions
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network-specific arrangements or communication protocols supporting networked applications
    • H04L67/20Network-specific arrangements or communication protocols supporting networked applications involving third party service providers
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W4/00Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02Services making use of location information
    • H04W4/021Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences

Abstract

PROBLEM TO BE SOLVED: To deliver an accurate, relevant, and timely advertisement (ad) to a consumer based on position estimation at the time of delivery. In a mobile advertising platform, a mobile user's location and other information is translated into an indication of the mobile user's intent. A predefined location associated with the business / brand name is generated and the mobile ad request is processed to determine if the associated mobile device triggered any of these predefined locations. If it is determined that the mobile ad request triggered one or more pre-defined locations, then relevant information is provided regarding the triggered location and the ad is selected based on the triggered location and other factors. It The request with the relevant information about the triggered location is also a product on the location marketplace where mobile advertisers can bid through auctions. [Selection diagram] Figure 8A

Description

This application claims the priority of the following applications: `` Method an, filed on May 19, 2014
d Apparatus for Visualizing Real-Time Location-Based Events, U.S. Provisional Patent Application No. 62 / 000,494, filed
U.S. Provisional Patent Application No. 62 / 000,496, entitled `` ing Mobile Users Based on Store Visits '', 20
`` Method and Apparatus for Increasing Store Visitation Re, filed on May 19, 2014
US Provisional Patent Application No. 62 / 000,497 entitled `` sponses to Location-Based Mobile Advertising ''
No., `` Method and Apparatus for Modeling and Using Mobil, filed on May 19, 2014
U.S. Provisional Patent Application No. 62 / 000,499 entitled `` e User Intent Profile in Location-Based Mobile Advertising, '' filed on May 19, 2014 with
Provisional Patent Application No. 62 / 000,501 entitled `` nd Using IP regions in Location-Based Mobile Advertising, '' `` Method and Apparatus for Geo-Fencing, filed on October 22, 2014.
U.S. Provisional Patent Application No. 62 / 066,912 entitled "Using Map Overlay", U.S. Provisional Patent Application No. 62 / 067,965 entitled "Method and Apparatus for Mobile Advertising Using 3D Geo-Fencing" filed October 23, 2014 Filed on February 24, 2015, entitled "Methods and Apparatus f
or Marketing Mobile Advertising Supplies ”, US Provisional Patent Application No. 62 / 119,807.
The entire contents of each of these patents and patent applications are incorporated herein. This application was also filed with U.S. Patent Application No. 13 / 867,025, filed April 19, 2013, U.S. Patent Application No. 13 / 867,029, filed April 19, 2013, filed on even date herewith. `` System and Method fo
r Marketing Mobile Advertising Supplies, '' System and Method for Visualizing R
eal-Time Location-Based Events, '' System and Method for Estimating Mobile Devi
Also related to a US patent application entitled "ce Locations." The entire contents of each of these patents and patent applications are also incorporated herein.

The present invention relates to systems and methods for serving mobile advertising, and in particular, for marketing location-based mobile advertising.

Mobile applications are increasingly sending device location information to service providers to enable location-based services (LBS). Thus, in mobile advertising, advertisers are interested in delivering relevant advertisements to a user's mobile device based on location. Mobile advert
Ising supplies are locations that are priced and sold (eg, supplies at a particular location are more marketable than other locations).

As mobile advertising becomes more prevalent, different pricing models have been developed based on different strategies for purchasing mobile advertising campaigns that are tailored to the advertiser's budget. Examples of mobile advertising pricing models include cost-per-mille (CPM),
There are models called cost-per-install) and CPC (click-per-click). They are,
Although part of the basic mobile advertising pricing model, advertisers choose these pricing models to promote their products or businesses on mobile devices.

CPM advertising models are sometimes referred to as "pay-per-impressions." CPM simply means "cost per 1,000" in modern English. In a CPM campaign, an advertiser pays a promised bid when an ad is shown on a mobile device every 1,000 times. Also, CPM advertisers pay for impressions, not clicks or installs, and they tend to use mobile ads primarily to increase brand awareness.

CPI, also known as cost-per-acquisition, charges advertisers every time a mobile ad ("ad") leads to a conversion. This may be, for example, whether the user actually purchases an object, downloads an app, or performs another action desired by the advertiser. Thus, CPI campaigns can help deliver a predictable return on SMEs with limited marketing budgets in their advertising investments.

In the CPC model, advertisers pay per click (also known as PPC), regardless of whether the click resulted in a conversion. Ads are served to mobile device users based on a combination of the ad's click-through rate (CTR) and the advertiser's bid.

With any pricing model, the price of an ad campaign needs to be determined based on relevant factors. For example, many businesses have specific physical locations that sell goods and have been in or near stores or want to target mobile users who are in or near stores. Also, each business has its own characteristics, which can affect the willingness to pay for a particular ad. For example, a business is a fast food restaurant selling fast food, or a car dealer selling cars. Fast food is much cheaper than cars,
Fast food is purchased more often than cars. Further, for a particular business, the ad may be priced differently based on how likely a particular mobile user is to respond to the ad. Accordingly, a method and system for serving mobile ads that considers these and other factors will deliver accurate, relevant, and timely advertisements (ads) to consumers based on location estimates at the time of delivery. It is necessary.

FIG. 1 is an explanatory diagram of a packet-based network according to an embodiment. FIG. 4 is an illustration of a computer / server that performs one or more methods and / or provides one or more systems on an advertising platform according to embodiments. FIG. 2 is an illustration of a geofence definition system according to a particular embodiment. It is explanatory drawing of the simple geofence in the shape of a circle. FIG. 4 is an illustration of one or more polygon geofences defined for a geographic environment around a store according to certain embodiments. 4 is a table illustrating an example of a geofence stored in a geofence database according to certain embodiments. FIG. 5A is an illustration of a polygon geofence overlapping a main road according to certain embodiments. FIG. 5B is an illustration of a virtual rectangle created to include the geofence of FIG. 5A according to certain embodiments. FIG. 5C is a second illustration of a virtual rectangle created to include the geofence of FIG. 5A according to certain embodiments. FIG. 5D is an explanatory diagram of a method of drawing a line segment indicating a road zone according to a specific embodiment. FIG. 4 is an explanatory diagram in which various types of businesses are stacked on each other in a high-rise building complex. FIG. 4 is an illustration of a 2-D polygon geofence triggered by a mobile user location on the 10th floor of a high-rise complex according to certain embodiments. FIG. 3 is an illustration of a 3-D reinforced geofence reflecting a single floor, multiple floors, and / or aerial space or volume within or around a tall building complex according to certain embodiments. FIG. 4 is an illustration of a virtual tube geofence extending along the length of some or the entire flight path of a commercial flight according to certain embodiments. FIG. 1 is an illustration of a request processing system for processing a mobile advertisement request received from a network according to certain embodiments. 5 is a flowchart illustrating a method performed by a request processing system according to a particular embodiment. 5 is a flowchart illustrating a location process for generating location data according to certain embodiments. 4 is a flowchart illustrating a geofencing process for determining whether to trigger one or more predefined locations in a geofence database according to certain embodiments. 5 is a flowchart illustrating a process for determining whether any of the triggered geofences should be excluded or discarded according to certain embodiments. 9A-9C are block diagrams illustrating a portion of the content of an advertisement request at each stage of processing by a request processing system according to a particular embodiment. FIG. 1 is an illustration of a real-time advertising event visualization system according to a specific embodiment. FIG. 4 is a flow diagram of a real-time advertising event visualization system interacting with other systems / services locally or via a network according to certain embodiments. FIG. 2 is an illustration of a mobile user in an overlapping area of two geofences and on two different businesses according to certain embodiments. 4 is a table illustrating some examples of real-time location-based events stored on digital storage according to certain embodiments. 13A to 13D are explanatory diagrams of a real-time location-based event displayed on a display device. FIG. 4 is an illustration of an IP region system provided by a computer / server system according to certain embodiments. 5 is a flowchart illustrating a method performed by the IP region system to derive an IP region for each IP address according to a particular embodiment. FIG. 11 is an explanatory diagram showing an IP region example created using location information from a plurality of advertisement requests according to a specific embodiment. FIG. 4 is an explanatory diagram illustrating an example of an IP region for a large-scale facility such as an airport according to a specific embodiment. FIG. 6 is some examples of IP regions stored in a database as a spatial index along with associated IP addresses and other information such as respective centroids according to certain embodiments. FIG. 1 is an explanatory diagram of an advertisement server system according to a specific embodiment. 5 is a table illustrating a retargeting database according to a particular embodiment. 4 is a table of example location-based events according to certain embodiments. 9 is a table example of a matching criterion for an advertisement document according to a specific embodiment. 4 is a table example of a list of location information, request times, advertisement categories, and mobile user responses for fulfilled advertisement requests according to certain embodiments. FIG. 4 is a block diagram illustrating an example of a statistical result according to a particular embodiment. 5 is a flowchart illustrating a method for selecting an advertisement document taking into account a plurality of factors according to a specific embodiment. 6 is a table illustrating selection factors associated with various advertising documents according to certain embodiments. FIG. 3 is an illustration of a mobile advertising market according to certain embodiments. FIG. 6 is a flowchart of a method performed by an online marketer to evaluate a request with relevant information according to certain embodiments. FIG. 1 is an illustration of a store visit lift (SVL) system according to certain embodiments. 5 is a flowchart illustrating a method for increasing a store visit response to a location-based mobile advertisement. FIG. 4 is a block diagram illustrating a statistical example of a preselected panel of mobile users according to certain embodiments. 5 is a table illustrating examples of mobile device data according to certain embodiments. FIG. 4 is a block diagram illustrating exemplary statistical results derived by an SVL system according to certain embodiments.

The present invention relates to providing a mobile advertising platform. The platform translates the mobile user's location and other information into indications of the mobile user's intent, and the advertiser can accept the mobile ad request or place a bid on the mobile supply with such indications. You can go. In certain embodiments, predefined locations associated with the business / brand name are created and a mobile ad request is determined whether the associated mobile device has triggered any of these predefined locations. . If it is determined that the mobile ad request has triggered one or more predefined locations, it will be tagged with relevant information about the triggered location and the ad will be selected based on the triggered location and other factors Is done. The request with relevant information, including the triggered location, may be a location marketplace item that is auctioned to a mobile advertiser who can bid on the triggered location.

In certain embodiments, a computer system is coupled to a packet-based network and processes an advertisement request according to an advertisement request processing method. The method for processing an advertisement request includes receiving an advertisement request associated with a mobile device from a packet-based network, and estimating a location of the mobile device based on information in the advertisement request. The method for processing an advertisement request determines whether the estimated location of the mobile device triggers one or more predefined locations in a geofence database stored in storage, and determines one or more triggered locations. And generating an advertisement request with relevant information including

In certain embodiments, estimating the location of the mobile device is based on the
It involves translating the IP address into a probabilistic representation of where the mobile device can be. In certain embodiments, the IP region for a particular IP address is derived from requests made during a particular time period, each of the plurality of requests being based on a particular IP address and GPS-based location data (eg, longitude / Latitude or LL). The particular IP address may be associated with a stationary device, such as a router, that allows the mobile device to connect to a packet-based network (eg, the Internet) via WiFi. Thus, when a new request arrives (or does not include the LL) with this particular IP address and unreliable LL, the IP region is used as the location where the new request can be and the advertisement can be served based on that location . In certain embodiments, the IP region has a center point and a size, and the center point of the IP region is used as the approximate location of the mobile user associated with the new request, and the reciprocal of the size serves as a measure of confidence . Alternatively, the entire boundary of the IP region can be used as an area where mobile users can exist.


In certain embodiments, the one or more triggered locations include a first location, where the first location is at least one of a location type, a category, a brand name, and a location identifier.
Represented by one. The location type is selected from a plurality of location types, such as a business center, a business premises, a business area, and each is partially or entirely associated with a single business.

In certain embodiments, the method includes searching an ad database for one or more ads that match the request with the relevant information, selecting an ad from the one or more ads that match, and And transmitting to the network. Each matching advertisement of the one or more matching advertisements is associated with one or more locations of the one or more triggered locations in the request with the relevant information.

In certain embodiments, the advertisement request includes an identifier that identifies the mobile device or its mobile user, and selecting an advertisement from the one or more matching advertisements is based on the mobile user intent profile database database. Including referencing a tent profile.

In certain embodiments, selecting an ad from one or more matching ads refers to a retargeting database that stores information about mobile users who have visited a geographic location corresponding to one of the triggered locations. Including doing.

In certain embodiments, selecting an advertisement from the one or more matching advertisements includes referencing statistical data associated with at least one of the one or more triggered locations.

In certain embodiments, the request with relevant information further includes a price for each of the one or more locations.

In certain embodiments, the method of processing an advertisement request further comprises sending the advertisement request with the relevant information to a packet-based network,
The method may further include receiving a bid including a bidder identifier, a request identifier, and a bid price for one of the one or more triggered locations. The ad request processing method searches the advertisement database for one or more matching advertisements that match the request with the relevant information, selects one advertisement from one or more matching advertisements, and selects one or more matching advertisements. And deciding whether to accept a bid based on the bid price and the price associated with the bid.

In certain embodiments, estimating the location of the mobile device comprises:
Responsive to the set of geographic coordinates of the ad request that does not meet the predefined criteria, determining whether the ad request includes the set of geographic coordinates that meets the set of predefined criteria; Judge whether the IP address is included, query the IP region database using the IP address, find a matching IP address in the IP region database, and find the matching IP as the estimated position of the mobile device
Including utilizing geographic coordinates associated with the address. The geographic coordinates are related to the geographic area, and the reliability depends on the size of the geographic area.

In certain embodiments, the IP region for a particular IP address is derived from multiple requests made over a particular time period, including the particular IP address and GPS-based location data (eg, longitude / latitude or LL). Is done. The specific IP address is associated with a stationary device, such as a router, that allows the mobile device to connect to a packet-based network (eg, the Internet) via WiFi. Therefore, when a new request arrives (or does not include the LL) with this particular IP address and unreliable LL, the IP region will be used as a possible location for the new request and the ad will be served based on this estimated location Is done. In certain embodiments, an IP region has a center point and a size, and the center point of the IP region is
Used as the approximate location of the mobile user associated with the new request, the inverse of the size serves as a measure of confidence for the location. Or, the entire boundaries of the IP region
Used as an estimated area for mobile user locations.

In certain embodiments, a first computer system coupled to a packet-based network includes a real-time location based event.
nt) is executed. A packet-based network includes one or more second computer systems. The method comprises: receiving a first advertisement request associated with a mobile device from a packet-based network; estimating a first location of the mobile device based on information in the advertisement request; Querying a geofence database in a storage device using the estimated first location in response to the first location triggering the first geofence; Updating aggregated historical / statistical data for the first business
Triggering a first geofence associated with the geofence; and transmitting information related to the first geofence to the one or more second computer systems in the packet-based network; The information allows the one or more second computer systems to visualize the triggering of the first geofence by the estimated mobile device.

In certain embodiments, updating the aggregated data of the first geofence comprises: determining a number of one or more visits made to the first business by a mobile user during a predefined time period; The number of times a geofence associated with a brand of one business has been triggered during the predefined time period and the number of times a geofence associated with a category of the first business has been triggered during the predefined time period Including increasing.

In certain embodiments, the visualization method includes transmitting the aggregated data to the one or more second computer systems in response to a request from the one or more second computer systems. In addition.

In certain embodiments, the first business is associated with a second business, and wherein the visualization method comprises: creating a second business affinity data based on the updated aggregated history / statistical data of the first business. Further comprising updating

In certain embodiments, the visualization method comprises: receiving a second advertisement request associated with the mobile device from the packet-based network; and, based on information in the second advertisement request, Estimating a second location, querying the geofence database in the storage device using the second location, triggering the second geofence in the geofence database Updating the number of mobile users remaining in the first business to respond to the location. In particular, the second geofence is different from the first geofence.

In certain embodiments, a computer system coupled to a packet-based network via a wired or wireless network connection performs the SVL method to obtain location-based advertising statistics. The method comprises delivering a first digital advertisement to a first group of mobile devices via the packet-based network, wherein the first group of mobile devices includes location information, mobile device information and mobile user information. Receiving a first set of mobile device data associated with at least a portion of the mobile device data comprising location information indicating a response to the first digital advertisement in a second set of the mobile device data Identifying a second set of mobile device data, generating a statistical result using the second set of mobile device data, and storing the statistical result in a storage device.

In certain embodiments, the first group of mobile devices includes a pre-selected panel of mobile devices configured to periodically provide location information to one or more computer systems of the packet-based network; The first mobile device data set includes mobile device data associated with at least a portion of the preselected mobile device panel.

In certain embodiments, the first set of mobile device data is included in a request for a document from one or more second computer systems interacting with at least a portion of the first group of mobile devices. Including mobile device data.

In certain embodiments, the first set of mobile device data includes mobile device data provided by one or more second computer systems of the packet-based network. In the packet-based network, execute one or more software development kits that provide logic to control when mobile device data is transmitted to the first computer system.

In certain embodiments, identifying the second set of mobile device data comprises:
Determining whether any of the location information of the first set of mobile device data includes geographic coordinates corresponding to one or more geographic regions associated with the first digital advertisement. .

In certain embodiments, the statistical results include trends associated with one or more of a set of parameters consisting of age, gender, educational level, response time, mobile device make and model.

FIG. 1 illustrates, in a particular embodiment, a packet-based network 100 (sometimes referred to herein as a cellular network 101, coupled to the Internet (or the Web) 110, the Internet 110, and some or all of a computer / server 120. Cloud). The computer / server 120 may be wired Ethernet, optionally Power over Ethernet (PoE), WiFi, and / or multiple cellular towers 1
Internet 110 using cellular connection via cellular network 101 including 01a
Can be combined. The network also includes one or more network attached storage (NAS) systems 121, which are computer data storage servers connected to the computer network to provide data access to heterogeneous groups of clients. As shown in FIG. 1, one or more mobile devices 130 such as a smartphone or tablet computer are also coupled to a packet-based network via a cellular connection with a cellular network 101 coupled to the Internet 110 via an Internet gateway. Is done. If a WiFi hotspot (such as hotspot 135) is available,
The mobile device 130 can connect to the Internet 110 via a WiFi hotspot 135 using its built-in WiFi connection. Thus, mobile device 130 can interact with other computers / servers coupled to Internet 110.

The computer / server 120 connected to the Internet is connected to the publisher (publishe
r) one or more publishers interacting with a mobile device running the app provided by r), one or more advertising intermediaries or advertising networks acting as intermediaries between publishers and advertisers, One or more ad servers for selecting and sending advertisement documents to the publisher for posting on a mobile device; one or more computers / servers operating ad exchanges; supplies) on one or more computers / servers that post on the advertising exchange, and / or one or more computers that monitor the advertising exchange or bid on mobile supplies listed on the advertising exchange. Including advertisers. When a publisher interacts with a mobile device, the publisher can identify features of the mobile device, certain information about the user,
Generate a mobile offer, which is a request for an advertisement (ad request) that includes raw location data and the like associated with the mobile device. Publishers may post mobile offers on an ad exchange for bidding by advertisers or their agencies, send mobile offers to advertising agencies or advertising intermediaries, or supply themselves.

Advertisers, agencies, publishers and ad agencies can also purchase mobile supplies through Ad Exchange. Ad networks and other entities also purchase advertising from exchanges. Typically, ad networks aggregate inventory from various publishers and sell them to advertisers for profit. Exchange is a digital marketplace where advertisers and publishers can buy and sell advertising space (impressions) and mobile inventory (ad inventory). The price of an impression is determined by a real-time auction through a process called real-time bidding. That is, there is no need for a human clerk to negotiate a price with the buyer. This is because the impression is simply sold to the highest bidder. When a mobile device loads an app or web page, these processes run in milliseconds.

Advertisers and agencies can use a demand-side platform (DSP), which is software that uses a particular algorithm to decide whether to purchase a particular supply. Many ad networks now also offer certain DSP-like products and real-time bidding capabilities. Online and mobile publishers have access to inventory through exchanges, so it is more economical for many advertisers to purchase ads using DSPs.

An ad server is a computer server. For example, a web server backed by a database server that stores advertisements used in online marketing and places them on websites and / or mobile applications. When a website or web page is visited or refreshed by a user, the content of the web server is constantly updated so that the website or web page where the ad is displayed contains new ads. (Eg, banner (still image / animation) or text). In addition to choosing an ad server to serve to your users,
The ad server also manages the advertising space on the website and / or provides the advertiser with an independent counting and tracking system. Therefore, the ad server has the ad /
Serve, count them, select ads that bring the most money to a website or advertiser, and monitor the progress of different advertising campaigns. The ad server is a publisher ad server, an advertiser ad server, and / or an ad mediator ad server. An ad server is also a part of the same computer or server that operates as a publisher, advertiser, or ad intermediary.

Ad serving also includes a variety of tasks, such as counting the number of impressions / clicks in your ad campaign and generating reports.
ROI). Ad servers can run locally or remotely. A local ad server is typically operated by a single publisher, serves ads to the publisher's domain, and allows for fine-grained creative, format, and content management by the publisher. Remote ad servers can serve ads across domains owned by multiple site publishers. By serving ads from one central source, advertisers and site publishers can track the delivery of online ads and control the rotation and delivery of ads on the web.

The computer / server 120 is a server computer, client computer, personal computer (PC), tablet PC, set-top box (STB), personal digital assistant device (PDA), web appliance, network router,
Switches or bridges include computing devices that can execute instructions that specify the actions that the computing device should take. As shown in FIG. 1, some of the computers / servers 120 are coupled to one another via a local area network (LAN) 110 that is coupled to the Internet 110. Also, each computer / server 120 referred to herein may individually or jointly execute instructions to provide one or more systems discussed herein, or may implement the methods discussed herein or Perform one or more of the functions or include publishers, advertisers, advertising agencies, advertising intermediaries, ad servers, ad exchanges, etc., which use the systems, methodologies, and functions described herein. I do.

FIG. 2 illustrates a computer / computer that can be used to perform one or more methods by executing certain instructions and / or to provide one or more systems of the advertising platform discussed herein. FIG. 2 shows an explanatory diagram of a server 120 (computer system). Computer / server 120 can operate as a standalone device or as a peer computing device in a peer-to-peer (or distributed) network computing environment. As shown in FIG. 2, the computer / server 120 includes one or more processors 202
(Eg, a central processing unit (CPU), a graphics processing unit (GPU), and / or a digital signal processor (DSP)) and a main memory 204 coupled to each other via a system or system bus 200. Computer / server 120 can communicate with processor 202 via static memory 206, network interface device 208, storage device 210, one or more display devices 230, one or more input devices 234, and system bus 200. It further includes a signal generation device (eg, a speaker) 236.

In certain embodiments, display device 230 includes one or more graphics display units (eg, a plasma display panel (PDP), a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)). The input device 234 includes an alphanumeric input device (for example, a keyboard),
Includes a cursor control device (eg, mouse, trackball, joystick, motion sensor, or other pointing device). The storage device 210 includes a machine-readable medium 212 having stored thereon instructions 216 (eg, software) that enable any or more of the systems, methodologies or functions described herein. The storage device 210 may also store data 218 used and / or generated by the system, methodology or function.
The instructions 216 (eg, software) can be fully or partially loaded into the main memory 204 or the processor 202 (eg, in the processor's cache memory) during execution by the computer / server 120. Thus, main memory 204 and processor 1102 also constitute a machine-readable medium.

Although machine readable medium 212 is illustrated in embodiments that are a single medium, the term “machine readable medium” refers to a single medium or multiple media capable of storing instructions (eg, instructions 1124). It should be construed to include media (eg, centralized or distributed databases, or associated caches and servers). The term "machine-readable medium" refers to a computer
Instructions for execution by server / server 120 (eg, instructions 216) can be stored and interpreted to include any medium that causes computing device 1100 to perform one or more of the methodologies disclosed herein. Should. The term "machine-readable medium" includes, but is not limited to, data repositories in the form of solid-state memory, optical media, and magnetic media. In certain embodiments, the instructions 216 and / or data 218 are stored on the network 100 via a network interface device 208 that provides a wired and / or wireless connection to a network, such as a local network thereof, via a computer / Through a type network connector 280a, it is connected to an area network 111 and / or a wide area network (eg, the Internet 110). Instructions 216 (eg, software) and / or data 218 can be sent or received via network interface device 208.

FIG. 3 is an illustration of a geofence definition system 300 provided by the computer / server system 120 according to certain embodiments. As shown in FIG. 3, when the processor 202 of the computer / server system 120 executes the geofence definition software program 301 loaded on the main memory 204, the geofence definition system includes a boundary definition module 310 and a spatial index generation module 320. A system 350 that utilizes a plurality of databases that store data used and / or generated by the geofence definition system 300 and stores a jenfence generated by the spatial index generation module 320; Historical / statistical (H / S) data and POI (Poin
t of Interest) directory, and a database 380 for storing map data. Any or all of these databases can be
The process 202 can be accessed via the network interface device 208.

The boundary definition module defines a virtual perimeter of the defined area that reflects the real world geographic area for mobile advertising. The defined area according to certain embodiments may be a static circle around the business location. I provide a list of businesses and their locations
Fences obtained using an offline index database such as nfoUSA (www.infousa.com) or specified by marketers using predefined boundaries such as neighborhood boundaries, school zones, parcel boundaries Areas, defined areas according to particular embodiments, can also be calculated dynamically and can have any shape that changes depending on the time of day, day of the week, or other variables. They are co-pending “Method and Apparatus f
or Dynamic Fencing, which is described in U.S. Patent Application No. 13 / 867,025, filed April 19, 2013, and incorporated herein by reference.

In certain embodiments, the defined regions include locations calculated by the boundary definition module 310 using business meta-information and / or geographic information. As shown in FIG. 3, the boundary definition module 310 has access to a (POI) directory (eg, InfoUSA) that provides a list of POIs and their corresponding brand names, addresses, and geographic locations. The boundary definition module 310 also has access to map data 380 that provides information about the perimeter of the POI in the POI directory. The boundary definition module 310 generates one or more locations for each POI in the form of a set of geographic coordinates that define the perimeter of the one or more locations based on the POI information, for example.

In certain embodiments, the boundary definition module 310 generates or defines one or more locations for each of the plurality of POIs, taking into account map data around the POI (eg, an open street map). For example, as shown in FIG. It becomes a circle 402 around 401. But,
As shown in FIG. 4A, the circular fence surrounds the main arterial road, the residential area, and the area opposite the main arterial road. Ads served to mobile devices in these areas can help people living near the store, driving on the highway, and those on the other side of the highway already know what the store offers, Ads served to mobile devices in these areas are largely ignored because they may or may not respond to mobile ads associated with the store.

Thus, instead of a geofence based on the radius around the centroid of a business location, the boundary definition module 310 according to certain embodiments uses map data to define places of more interest to mobile advertisers. As shown in FIG. 4B, the one or more polygons can be defined according to the geographical configuration and perimeter of the store, the first polygon 410 around the store building, the first polygon 410 around the building and its parking lot. Such as a second polygon 420 and / or a third polygon 430 around a shopping or business area containing stores and other stores.

In certain embodiments, different types of locations are provided to the POI so that the mobile advertiser can offer different ads or different prices for ads delivered to the mobile device that triggered these different types of locations. Can be defined. For example, the first around a store building
An ad request relating to a mobile device located inside the polygon 410 of the advertiser is more valuable to the store owner or competing business, and therefore an advertisement for a mobile device that is in the shopping area (polygon 430) but not in the store You may set a higher bid price than requested. Alternatively, polygon 430 may be set higher by the store owner to attract mobile users in the business area than polygon 410, which indicates that the mobile user is already in the store. In certain embodiments, these three types of locations are defined by extracting building polygons, parking lot polygons, and land use polygons from local and national GIS systems. In certain embodiments, some or all of the locations can be manually defined with the help of computer annotation tools to align the geofence with the surrounding perimeter information surrounding the actual building and the envisioned business. Refer to some external maps or / and satellite data to secure.

In certain embodiments, different types of locations relevant to the business offered to mobile advertisers include, for example, (1) a business center (BC) represented by polygons corresponding to the perimeter of the building (eg, FIG. First polygon 410). (2) Business premises (BP) represented by polygons corresponding to the periphery of the business building and adjacent parking lot (eg, second polygon 420 in FIG. 4B). (3) Include a business area (BR) or area (eg, third polygon 430 in FIG. 4B) represented by a corresponding polygon around the shopping center or business or commercial area where this business resides. When a business center is triggered, it can be inferred that users actually have an interest in the business by visiting them. Business premises triggers indicate a willingness to visit a business, but are not as strong as business center triggers. When a user triggers a business area, the intent is considered valid but less likely than triggering a business premises.

The spatial index generation module 320 generates a spatial index representing the area defined by the boundary definition module 310 and creates a geofence for storing in the geofence database 350. The geofence database 350 is a spatial database that supports the processing of a spatial query, and calculates the distance between two points or determines whether a point is in a spatial region of interest. The spatial index generation module is based on the conventional spatial indexing method or / and the method and apparatus for Ge, filed on April 19, 2013.
The indexing method described in U.S. Patent Application No. 13 / 867,029 entitled "graphic Document Retrieval", which is incorporated herein by reference, may be used. FIG. 4C illustrates an example of a geofence stored in database 350, according to certain embodiments. As shown, the Almaden store Costco has three different types of locations.
US / CA / Almaden / BC is the spatial index a1, a2, ..., ai with polygons around the store
It is a business center (BC) represented by. The location US / CA / Almaden / BP is a polygon around the large premises of the store including the parking lot, and is represented by spatial indexes b1, b2, ..., bj. The location US / CA / Almaden / BR is a polygon around a shopping center including stores and other stores, and is represented by spatial indexes c1, c2, ..., ck. Figure 4C also shows store T
.J. Maxx has three types of locations, and Trader Joe's in stores has at least one business center location associated with it. As shown in FIG. 4C, each geofence entry in database 350 includes a spatial index associated with each location (and other information about each location), such as the name / brand associated with the location, Category, location identifier identifying a particular region (eg, city, district, etc.), location type, and / or one or more document IDs identifying one or more advertising documents related to name / brand or location It is.

The geofence definition system 300 further extracts a map data for the highway near the defined geofence, overlays the map data on the geofence, and creates an expanded geofence module 330. Can be included. For example, as shown in FIG. 5A, the boundary definition module 310
Generate a Geofence 500 for 01. The geofence 500 in this example shows that the business 501's mobile advertising campaign is aimed at attracting mobile users to visit other businesses or to work at nearby office complexes, so restaurants 501 And a polygon that encompasses other businesses around the restaurant 501. But,
The advertising campaign wants to exclude mobile users who drive on highways 512, 514, and 516 of the GeoFence 500. The reason is that these mobile users are moving fast and are less likely to respond to restaurant mobile ads by returning to the restaurant.

Thus, in certain embodiments, the map overlay module 330 creates a virtual rectangle 503 that includes the geofence 500. The rectangle 503 is the smallest rectangle including the entire geofence 500 as shown in FIG. 5B. Next, the map overlay module 310 creates a virtual rectangle 503
Map data associated with the main roads that overlap, eg, roads 512, 514, and 516, and converts the map data into line segments. As shown in FIG. 5B, portions of the main roads 512, 514, and 516 that overlap the virtual rectangle 503 are converted into line segments AB, CD, DE, EF, FG, and HI. Geofence
The 500 together with the line segment forms an expanded geofence of the restaurant 501 and can be used to evaluate whether the mobile user associated with the ad request is a mobile on a highway.

Instead of, or in addition to, lines drawn along or near the median median, arterial roads use line segments, for example, drawn along the opposite edge of the road. Can also be represented by a road belt. As shown in FIG. 5D, the expanded geofence for business 505 has a circle 5 drawn around business 505 and line segments 532, 533, 542 and 543.
Including 06. Lines 532 and 533 are drawn along the edge of Hwy237 on two opposite sides of median 535 on highway 237 along the edge of line highway Hwy237,
Lines 542 and 543 extend along the edge of highway 82 to median 545 of highway Hwy82.
Two opposite sides are drawn. Therefore, mobile devices located in the main road zone are considered to be moving along the main road. Also, depending on which side of the highway the mobile device is located, its distance from the highway can be measured from the end of the highway on the same side.

Figures 4A-5D are useful for location-based advertising where businesses occupy separate geographic regions
Here is an example of a two-dimensional (2D) geofence. They are not well suited when different types of businesses are stacked on top of each other in a skyscraper as shown in 6A. For example, FIG.
6A, triggered by a user location 601 on the 10th floor of the skyscraper shown in FIG. 6A.
The 2D polygon geofence 600 can be used to select ads for a particular business that occupies a particular floor of the skyscraper if multiple businesses in the skyscraper target the same geographic fence 600. Not easy to use.

In certain embodiments, the geofence definition system 300 further includes a three-dimensional extension module 340 that provides an enhanced geo-fencing solution to a targeted three-dimensional (3-D) location. Instead of, or in addition to, the 2D polygon geofence of FIG. 6B, as shown in FIG. It reflects a single floor, multiple floors, and / or aerial space or volume in the perimeter, respectively.

In certain embodiments, a three-dimensional geofence is a volume (or campaign space) surrounded by a digital fence, for example, encompassing real-world objects (eg, parts of buildings, underground spaces, mountain peaks, etc.) ( warp around) A three-dimensional polygon fence or the like, as shown in FIG. 6C, which may be a volume / space that can be designated by a marketing person, such as a high-rise shopping mall floor. For example, a simple 3
A D Geofence may be represented by a 2D stamp (eg, its projection on the ground).
D-stamps include 2-D polygons or arbitrarily shaped 2-D areas and elevation sections (for example, from the third floor to the fifth floor of a building), all of which can be moved according to date and time, day of the week, time, etc. Can be targeted. For example, it can be dynamically or otherwise included or excluded by an advertising campaign, depending on the specifications of the building section campaign.

In certain embodiments, the 3D extension module 340 determines for each POI for which a geofence is created whether the POI is suitable for 3D geofencing. Such a determination may be based on whether the POI is on a particular floor of a multi-storey building, or whether an advertising campaign for the POI has required 3D geo-fencing. In certain embodiments, a POI that is not located in a skyscraper may want 3D geofencing. For example, an operator may want to target mobile users in flight from city A to city B. As shown in FIG. 6D, the three-dimensional geofence can include a virtual tube 650 that extends along a portion or the entire length of a flight path 660 of one or more flights flying from city A to city B. An airplane in the flight path 660 triggers the 3D geofence 650 instead of, or in addition to, the 2D geofence 680 for ground businesses under the airplane. The direction of flight is derived from multiple advertisement requests from the same mobile device, providing a more appropriate advertisement based on which of the two end cities of the flight path is the mobile user's destination be able to.

FIG. 7 illustrates a request processing system provided by a computer / server system 120 for processing mobile advertising requests received from a network 100 according to certain embodiments.
FIG. As shown in FIG. 7, the processor in the computer / server system 120
202 is an advertisement request processing software program 701 loaded in the main memory 204
Provide a request processing system 700 that includes a validation module 710, a location module 720, a geo-fencing module 730, and an annotation module 740. A system 700 utilizing a plurality of databases that store data used and / or generated by the request processing software program 701 may include a database 750 for storing geofences generated by the geofence definition, and a geofence definition system 300. Database 750 for storing generated geofences, database 760 for storing historical / statistical data, database 770 for storing business value information, WiFi
It includes a database 780 that stores the IP regions corresponding to each IP address in the collection of hotspots 135 and cellular tower 101a. Any or all of these databases may be located on storage 210 or on other servers / computers 120 and / or NAS 121 in network 100, and process 202 may be performed via network interface device 208 You can access these databases.

FIG. 8A illustrates a method 800 performed by a request processing system 700 according to certain embodiments.
It is a flowchart which shows. As shown in FIG. 8A, the system 700 receives an advertisement request over a connection 208, 208a that connects to a network (eg, the Internet) (810).
). An advertisement request is made from a web service provider with which a mobile user has initiated an interaction using a mobile device 130 via one or more web services or applications provided by a mobile publisher or web service provider. Can come. Ad requests are sent to the supply-side platform (SS
P) may be initiated by a software development kit (SDK) provided by The advertising request may be provided, for example, by an advertising broker, an ad exchange, or any advertising service provider. The ad request can include, in addition to other information, latitude and longitude coordinates (LL), IP address (IP), postal code (ZC), as can be seen in FIGS. 9A-9C.
And / or includes location information for a mobile device that includes multiple location components such as a city state name (CS). Also, the advertisement request can include altitude coordinates that can be used to indicate the altitude of the mobile device.

In certain embodiments, the validation module 710 checks the validity and consistency of the location components and validates the location information by excluding any invalid location components (820). Generally, LL is usually considered to be the most useful location component. However, if the user does not want to know his location information, the mobile application will typically have a coarse location such as, for example, an IP address, ZC (eg, entered by the user during registration), or CS Provide data only. Thus, mobile applications and publishers frequently provide LLs derived from geocoding software that convert ZC, CS and other points of interest into one representative LL. In one embodiment, such representative LLs are classified as "bad LLs." Bad LLs are for example:
1. ZC / CS Centroid 2. Any fixed point on the map (for example, (0,0) or any location)

In certain embodiments, the validation module 710 is an application that is filed on
By using the technique disclosed in a commonly owned U.S. patent application entitled "ystem and Method for Deriving Probabilistic Mobile User Locations," Not be provided for stage processing.

The location module 720 estimates 830 the location of the mobile device from the advertisement request and generates location data representing the mobile device location, such as geographic coordinates, or one or more estimated areas. The geofence module 730 queries the geofence database 750 using the location data to determine whether the location data triggers one or more predetermined locations in the database 750 (840). Geofence module 730, as discussed in more detail below,
Determine if any of the triggered locations should be excluded or discarded (850). The annotation module 740 attaches (860) relevant information to the advertisement request at the triggered location, as described in further detail below. The request with the relevant information can be on the same computer / server system 120 or on a different computer / server system 120 in the network 100, the ad serving system 1900 described below.
Etc. are provided to the advertisement distribution system. The ad distribution system can be an ad server, ad exchange or marketplace. The system 700 sends an advertisement request with relevant information to the advertisement distribution system via the network interface device 208 if the advertisement distribution system is on a different computer / server system.

FIG. 8B shows a location module (720) for generating location data (830).
5 is a flowchart showing a location process 830 executed by the server. As shown in FIG. 8B, the location module determines whether the verified location component includes a set of geographic coordinates (eg, LL), and whether the set of LLs is valid or geographically accurate. It is determined whether or not (821). A valid or geographically accurate set of LLs
If it is determined to be L (ie, true LL), the location module 720 uses the LL as location data to represent the estimated location of the mobile device. On the other hand, if the verified location component does not include the set of LLs, or if the set of LLs is not true, location module 720 determines whether the verified location component includes an IP address (823). If the verified location component includes an IP address, the location module determines whether the IP address is in the IP region database 780 (824). IP address is IP region database
If it is at 780, the location module is (826)
Generate location data using a derived IP region associated with the IP address. The location data may include geographic coordinates representing the IP region itself or its center using some function of the reciprocal of the size of the IP region as a measure of confidence. On the other hand, if the location data does not include an IP address, or if the IP address is not found or associated with a derived IP region in the IP region database, the location engine uses other location components (825
Generate location data or use an external IP vendor database for IP
Is converted to another location component, and the other location component is used (825) to generate location data (826). In certain embodiments, April 1, 2013
As described in commonly owned U.S. Patent Application No. 13 / 867,021, filed on May 9, entitled "Method and Apparatus for Probabilistic User Location," which is incorporated herein by reference in its entirety. , Location data generated using other location components includes one or more weighted estimated areas.

FIG. 8C is a flowchart illustrating a geo-fencing process 840 performed by the geo-fencing module 730 to determine whether location data triggers one or more predetermined locations in the database 750 (840). . As shown in FIG. 8C, the geo-fencing module 730 allows the mobile device 130 to move the mobile device 130 to a higher altitude location near a geographic area where 3D geo-fencing is more appropriate (eg, a commercial area with skyscrapers). It can be determined whether the location data indicates that
1)). If true, the geofencing module 730 will attempt to find a three-dimensional geofence in the database 750. The geofence can encompass or overlap the estimated location of the mobile device represented by the location data.
If not, the geofencing module attempts to find a two-dimensional geofence in the database 750 and may include or overlap the estimated location of the mobile device represented by the location data. A 2-D or 3-D geofence discovered in this way is said to be triggered by location data.

FIG. 8D is a flowchart illustrating a process 850 for determining whether to exclude or discard any of the triggered geofences according to a particular embodiment. For example, as shown in FIG. 8D, the geo-fencing module 730 determines whether any of the triggered geo-fences overlap a highway (851), and the mobile device 130 travels along one of the highways. Can be further determined (852). This may be, for example, that the mobile device sets within a boundary set for any one of the one or more highways, or within a predetermined distance from any of the one or more highways. This is possible by determining whether or not the location data indicates that the operation has been performed.
In certain embodiments, additional steps are taken to verify that the mobile device is traveling on a highway. For example, information such as location data and a timestamp associated with the current advertisement request is stored and used with the location data and timestamp of the next request associated with the same device to determine the speed of the mobile device. Triggered geofences that overlap a highway can be excluded or discarded if it is determined that the mobile device is traveling on a highway.
Or, if the advertising campaign is actually targeting a mobile device traveling on a highway, another geofence of the advertising campaign may be attached to the ad request.

In certain embodiments, as shown in FIG.9A, an advertisement request 901 received from the Internet by request processing system 700 includes other information and location information, for example, associated with a mobile device and / or a mobile device. Information about the mobile user, a timestamp indicating the time of the ad request request (eg, days, hours, minutes, etc.), one or more keywords indicating the type of advertisement returned to the mobile device, and / or the mobile user, mobile And / or other information related to the sender of the advertisement request. In certain embodiments, the location module 720
As shown in FIG. 9B, the location information is obtained from the advertisement request, the location information in the advertisement request is replaced with the location data, and the modified advertisement request 902 is obtained.
Generate The location module 720 can further convert the location data into a spatial index having the same meaning as the location data to facilitate use by the geo-fencing module 730.

In certain embodiments, as shown in FIG. 9C, if the location data triggers a predefined location or geofence, the annotation module 740 attaches the triggered location to the advertisement request or the advertisement request 901. Location information and ad requests within 9
Attach relevant information to the advertisement request 901 by replacing the location data in 02 with the triggered location. In some cases, location data may trigger multiple locations. For example, as shown in FIG. 4B, an ad request that triggers a BC place 410 for a Costco Almaden store may include a BR place 43 for any store in the same business area.
Trigger 0. Therefore, the ad request includes the BC location of the Costco Almaden store,
Relevant information can be attached to BR locations in one or more other stores in the same business area. Figure 9
As shown in C, each of the one or more locations or geofences includes one or both of a business name and a brand name with which the location is associated. For some businesses, only one is needed because the business name and brand name are the same. Each of the one or more locations is a category of product or service associated with the business / brand name (eg, grocery, general merchandise, park / recreation, sports, home improvement, etc.) and location of the location (eg, country / state / City), and location type (eg, BC, BP, or BR)
Some or all of them may be included in the advertisement request 910 with relevant information. In certain embodiments, a location or geofence is a recommended or threshold price for sending ads to a mobile device or bidding ads sent to a mobile device, as described in further detail below. including.

In certain embodiments, the location of the trigger is calculated to give the mobile advertiser another indication as to whether to bid on the supply and how to determine the bid price accordingly. Attached to. The trigger accuracy is the confidence of the mobile device's estimated location, and / or the relative proximity of the mobile device from the centroid of the location, or the relative proximity from the nearest side of the location, or the estimated area of the mobile device May be measured from the ratio of the portion overlapping the place. Therefore, ad requests associated with mobile devices that are known to be very close to the edge of the location, or associated with mobile devices that have one or more potential areas that do not substantially overlap the location, , Close to the place's centroid,
Alternatively, the one or more potential areas may be set at a different price than the advertising request associated with the mobile device that is substantially alleged to overlap the location.

FIG. 10A is an illustration of a real-time advertising event visualization system 1000 provided by a computer / server system 120 according to an embodiment. In certain embodiments, the real-time advertising event visualization system 1000 provides a map-based system for visualizing events based on a geo-fence or real-time location of a location triggered by a mobile advertising request to allow people to access Indicate such events as entering, staying in, or exiting a geographic location of interest. As used herein, the term "store" refers to a business or commerce location, or a location that performs a particular activity in a very particular geographic location, such as a shopping mall, bricks in a shopping mall, or the like. And mortar, office buildings, parks, gyms, schools,
Places like theaters, restaurants, etc. As shown in FIG.10A, the processor 202 in the computer / server system 120 executes the filter / aggregation module 1010, the application server module 1020, and the visualization module 1030 when executing the real-time visualization software program 1001 loaded in the main memory 204. including. The system 1000 uses multiple databases that store data used and / or generated by the real-time visualization software program 1001, and includes a filter / aggregation module 1010.
The database module 1050 that stores the aggregated data generated by the
oint of Interest) directory 1060 and a database 1070 for storing map data. Any or all of these databases are stored
The process 202 can access the network 100 via the network interface device 208, which can be located in 210 or in other servers / computers 120 and / or NAS 121 in the network 100.

FIG. 10B illustrates a real-time advertising event visualization system 1000 that interacts with other systems / services, either locally (ie, within the same computer / server system 120) or over a network (eg, the Internet 110 or local area network 111).
FIG. As shown in FIG.10B, the advertisement event visualization system 1000 includes a real-time computation pipeline in which an advertisement request processing system, such as the ad request processing system 700 discussed above, (E.g., the advertisement request 901 shown in FIG. 901) is processed to determine whether the advertisement request triggers any of the geofences stored in a geofence database (e.g., database 750). The advertisement request processing system 700 is the same computer / server 1 that also includes the real-time advertisement event visualization system 1000.
20 or may be provided by a different computer / server 120 in a local or wide area network. The advertisement request processing system 700 provides the real-time advertisement event visualization system 1000 with an annotated advertisement request 910 including location data and / or information such as triggered location or geofence. Alternatively, the ad request processing system 700 may send the modified ad request 902 (whether or not it has a triggered location or other information such as information about the geofence) to the real-time ad event visualization system 1000. May be provided.

In certain embodiments, the filter / aggregation module 1010, which is also in the real-time computation pipeline, filters the processed ad requests provided by the ad processing system 700 to filter real-time location-based events.
nt). For example, processed ad requests 902 or 910 may be filtered out based on relevant mobile user characteristics such as age, gender, and the like. As another example, if a mobile user happens to be located on two or more overlapping geofences associated with multiple stores at the same time, multiple geographic information or locations may be provided. In this case, only one of the geofences or locations can be compared by comparing data related to multiple geofences or locations to determine which of the multiple stores is more interested in mobile users. Retained, other geofences or locations are excluded. For example, as shown in FIG.
An advertisement request can be associated with a mobile user 130 within the overlap area of two geofences 1111 and 1112 for two different businesses. The ad request system 700 provides the user location and one or more geofences as triggered geofences or locations, and the filter / aggregation module 1010 determines its center point or centroid 110.
1 or 1102 selects a geofence closer to the mobile user 130 and records the presence of the mobile user within the selected geofence as a detected real-time location-based event. Alternatively, the filter / aggregation module 1010 may register both triggered fences as separate real-time location-based events.

The filter / aggregation module 1010 also uses other information of the triggered location, such as brand name, category, location type, accuracy of the trigger, etc., for one or more of the multiple triggered locations. You can select the triggered location. In certain embodiments, the probability of associating one or more target areas as a geofence or location may be provided. The filter / aggregation module 1010 can select the target region with the highest probability as being associated with the detected real-time location-based event. In another embodiment, the filter / aggregation module 1010 may perform a coin toss using the probability associated with the target region as a weighting factor to select a target region for a real-time location-based event. In a further embodiment, to select a target area for a real-time location-based event,
`` Method and Apparatus for Geographical Document Retrie, filed on April 19, 2013
The technique described in commonly owned US patent application Ser. No. 13 / 867,029 entitled "val" may be used.

The filter / aggregation module 1010 is further configured to aggregate historical / statistical data associated with the detected real-time location-based event, and store the aggregated historical / statistical data in the database 1050. For example, aggregated historical / statistical data can be used by mobile users for specific stores, specific brands, , The number of visits to a particular business category, and affinity data including the number of visits by mobile users to stores, brands, categories associated with the particular store, brands, business categories, etc. For example, each time a real-time location-based event is detected for a mobile user in a business, the count for the total number of real-time location-based events for that business / brand is increased by one count. At the same time, the count for the total number of real-time location-based events for each of the one or more categories to which the business / brand belongs also increases by one count.

In certain embodiments, in order to track the number of mobile users remaining at or leaving a particular store, the filter / aggregation module 1010 also includes a digital storage (
For example, the real-time location-based event is temporarily stored in the main memory 204, the static memory 206, or the storage device 210). FIG. 12 shows some examples of real-time location-based events stored on digital storage. For example, if a current real-time location-based event in a particular business is detected within a predetermined period (eg, one hour) after the occurrence of a previous real-time location-based event associated with the same mobile device, The user is deemed to have left the previous business. In this case, the user exit associated with the previous business (Us
er Exit) event is recorded. By subtracting the number of real-time location-based events associated with a given business over a given period of time from the number of user exit events associated with the same business within the same time period, the number of mobile users remaining in that business can be estimated. This number can also be displayed at the request of the operator. In some embodiments, the filtering / aggregation module may determine that one or more mobile devices that triggered a given business's geofence are then outside the applicable geofence (if it is inside another geofence). Or not), you can estimate the number of mobile users remaining in the business.

In certain embodiments, the current real-time location-based event for a given business is within a predetermined time period (eg, one hour) after the occurrence of a previous real-time location-based event associated with the same mobile device at the same business location. If detected, the current real-time location-based event is deemed to be the same as the real-time location-based event. This is because a mobile device user may simply be staying at a particular business for a while during a single visit. In such a case,
We do not aggregate historical / statistical data related to this business.

FIG. 10B shows a real-time computation pipeline utilizing an index database such as InfoUSA (www.infousa.com), which is based on a standard industrial classification (SiC
) Provide a list of businesses and their locations in the code, or a region specified by the marketer, such as a city, state, mall or shop, tourist attraction or a region associated with a specific zip code, and specific implementation Embodiments may be used in place of or in addition to the POI directory in the database 1070.

An application server module 1020 may be connected to a visualization module 1030 or a corresponding client installed on one or more client computer / server systems 120 in the network 100 via various protocols such as HTTP. Interact with the visualization application 1031. Application server 1020 is configured to bridge the real-time computation pipeline in system 1000 to a client system. The application server 1020 also queries the aggregated data from the storage 210. Visualization module 10 in the client system to maintain the data stream while maximizing the visual appeal of real-time location-based events
The 30 or corresponding visualization application 1031 may include custom logic that utilizes the map data in the database 1070 and / or the mapping techniques provided by another computer / server system 120. The application server 1020 organizes and processes the data associated with the real-time location-based event to provide aggregated historical / statistical data in response to the same request from the visualization module 1030 and / or the client system; , Visualization module 1030 and / or
Or the client system is the system 1000 or an input device in the client system
108 In response to input by an operator, for example, via input device 108 in system 1000 or a client system, real-time location-based events with selected aggregated historical / statistical data are displayed on display 107 of system 1000 or on a client system. To be displayed on a display device.

In certain embodiments, the center location of the selected location is used as the location of the detected real-time event. As shown in FIG. 13A, when displayed by system 1000 or a client system, a real-time location-based event can cause a dot on the map (eg, a map of the United States if specified by the operator) to flash. Each time a real-time location-based event is detected, a dot appears at the location associated with the real-time location-based event. Alternatively, if a location has a dot already at that location due to a past real-time location-based event, the dot flashes to indicate a new real-time location-based event.
As shown in FIG. 13A, different colored dots can be used to represent different businesses, brands, or categories, and in FIG. 13A, different patterns within the dots indicate color differences. The area 1310 on the screen may be, for example, the time since the client application was started or based on default settings or operator designation (eg, in the last 10 minutes).
Used to display the total number of real-time location based events. Thus, this total is the number of real-time events that change as new real-time location-based events are detected. Another area 1320 on the screen can be used, primarily for displaying historical / statistical data. In certain embodiments, various historical / statistical data may be displayed under different tabs that may be selected, for example, by a mouse click or keyboard input (not shown).

For example, each time the client application 1131 is started, a signal is transmitted from the associated client system to the application server 1120, and the application server 1120 is activated.
1120 begins to push real-time location-based events to client systems. The operator on the client server side can select history / statistical data to be displayed on the screen individually or simultaneously. For example, history / stats can be displayed under any one of a plurality of tabs, such as a stream tab, a brand tab, a category tab, and an affinity tab. The stream tab can be selected by default when launching the client application. Under the stream tab, as shown in FIG. 13, the business / brand / category name related to the real-time location-based event is displayed. These names change as new real-time location-based events are detected.

Under the brand tab, the brand name is displayed as shown in FIG. 13B. The client-side operator can scroll down to see all brand names corresponding to real-time location-based events. When an operator selects a brand name, for example, by a mouse click, history / statistical data associated with that brand may appear. As shown in FIG. 13B, Foster City Karaoke brand
If Karaoke is selected, the number 23 next to the brand name indicates the number of real-time location-based events since the application was started. Under the brand name, the day before,
You can also view other historical / statistical data, such as the number of visits to the brand by mobile users in the last 7 days or the last 30 days.

Similarly, the category name is displayed under the category tab. The client-side operator can scroll down to see all category names corresponding to real-time location-based events. When an operator selects a category name, for example, by a mouse click, history / statistical data associated with that category may appear. When you select a category name, the number next to the brand name indicates the number of real-time location-based events since the application was started. Under the category name, you can see other history /
Statistical data can also be displayed. In certain embodiments, when a category is selected, only location-based events associated with the selected category are displayed on the map, and new events within that category are displayed when the location-based event occurs in real time. Will be added.

In certain embodiments, the historical / statistical data also includes affinity data, such as a count of location-based events associated with the business having one or more characteristics similar to the selected business characteristics. Mobile advertisers can use affinity data to measure how often mobile users visit a particular business / brand compared to other businesses / brands in the same category.

As another example of displaying historical / statistical data, when the Affinity tab is selected, a small area under the Affinity tab is provided to select the brand / business name for which affinity information is required. Once the brand / business name is provided or selected, the historical / statistical data associated with the selected brand / business will be
Displayed with historical / statistical data associated with the / business. As shown in FIG.13C, if a particular brand is selected within that area, or if the operator zooms in on the business / store location for that particular brand, one or more associated with that particular brand A pop-up window 1330 that displays the brand history / statistics along with the category history / statistics can also be displayed.

In certain embodiments, an operator may select the geographic area, such as a country, state, city, or mall, to display real-time location-based events occurring in the geographic area. For example, FIG. 13D shows a real-time location-based event in California, the total number of real-time location-based events in California since the start of the client application displayed in screen area 1310, and a display in Shows aggregated / historical / statistical data related to real-time location-based events.

FIG. 14 is provided by a computer / server system 120 according to certain embodiments.
FIG. 3 is an explanatory diagram of an IP region system 1400. As mentioned above, the IP region can be used as the estimated location when the request has an IP address but no exact geographic coordinates. The IP region system 1400 derives an IP region corresponding to each IP address, respectively, using an advertisement request including an IP address received over a certain period (for example, several days). As shown in FIG. 14, the processor 202 of the computer / server system 120 includes an IP region region software program loaded into the main memory 204.
Executing 1401 provides an IP region generation module 1440 that includes a verification module 1410, a grouping module 1420, a centroid generation module 1430, and an IP region generation module 1440. System 1400 utilizes a plurality of databases that store data used and / or generated by IP region software program 1401, including a database 1450 that stores IP regions generated by centroid IP region generation module 1440. , Centroid generation module 14
It includes a database 1460 for storing the centroid generated by 30, a database 1470 for storing received advertisement requests, and a database 1480 for storing a POI (Point of Interest) directory. Any or all of these databases are stored in storage 21
0, or may be located on other servers / computers 120 and / or NAS 121 in the network 100, and the process 202 may access the network 100 via the network interface device 208.

FIG. 15 is a flowchart illustrating a method 1500 performed by the IP region system 1400 to derive an IP region for each IP address according to a particular embodiment. As shown in FIG. 15, when the advertisement request traffic arrives, the IP region system stores at least the location information of the advertisement request in the database 1450 (1510). A certain time (
After several days, for example), the IP region system 1400 performs the method 1500 to derive the IP region from the stored location information. The validation module 1410 determines that each set of LLs is a true LL
(I.e., representing the actual mobile device location)
The LL in the stored location information is checked (1520). Based on the determination, the grouping module 1420 groups the requests or their respective location information into different traffic groups as follows (1530).
1.T (IP, TLL)-Each request in this group has an IP and a valid geographically accurate LL.
2. T (IP, DLL_Static)-Each request in this group has an IP and a derived LL (corresponding to a static centroid derived from a geographic mapping (eg, city center point) or IP vendor mapping). derived LL).
3.T (IP, DLL_Dynamic)-Each request in this group has an IP and a derived LL that is not a static centroid.
4.T (NoIP, TLL)-Each request in this group has a valid geographically accurate LL, but no IP.
5.T (NoIP, DLL_Static)-Each request in this group has a derived LL corresponding to a static centroid, but no IP.
6.T (NoIP, DLL_Dynamic)-Each request in this group has a derived LL that is not a static centroid.
7. T (IP, NoLL)-Each request in this group has an IP, but no LL.

In a specific embodiment, the grouping module 1420 determines the location information if the location information has an IP address and the LL of the location information corresponds to the LL of a static centroid stored in the centroid database. Put in T (IP, DLL_Static) group. In certain embodiments, a static centroid associated with a well-known geographic area, such as a city, an area associated with a postal code, etc., is stored in a centroid database. Request LL for Static Centroid
In the case of one, it is likely that this LL is not a true LL, but was compiled by the LL mobile publisher by referring to the mobile user's city.

In certain embodiments, the grouping module 1420 determines that the location information comprises an IP address and the LL of the location information does not correspond to any of the static centroids in the centroid database, but the dynamic centroid does not. If corresponding to LL (ie, the centroid that occurs very frequently at this IP address has exceeded the threshold within a certain period of time-not covered by a known static IP centroid, but the IP to LL , Indicating that this is a database of another IP vendor that is used by the publisher to derive the location information), and put the location information into the T (IP, DLL_Dynamic) group.

In certain embodiments, the grouping module 1420 determines that the location information is T ( NoIP, DLL_Static) group. In certain embodiments, static centroids associated with well-known geographic regions, such as cities, regions associated with postal codes, etc., are stored in the centroid database. If the LL in the request corresponds to one of the static centroids, it is likely that this LL was not the true LL, but was compiled by the LL mobile publisher by deriving from the IP address.

In certain embodiments, the grouping module 1420 determines that the location information does not have an IP address and the LL of the location information does not correspond to any of the static centroids in the centroid database, but the dynamic centroid When corresponding to LL (ie, at this IP address, a centroid that occurs very frequently in a given time period, or a centroid-used by publishers to derive an LL from a given threshold over a given time period Pointing to another IP vendor's database (not covered by a known static IP centroid), put the location information into the T (NoIP, DLL_Dynamic) group

In certain embodiments, the grouping module 1420 determines that the location information has an IP address and the LL of the location information does not correspond to any of the static centroids in the centroid database, or the dynamic centroid database 1460 If the location information does not correspond to any of the dynamic centroids, the location information is put into a T (IP, TLL) group. Similarly, the grouping module 1420 may determine that the location information does not have an IP address and that the LL in the location information does not correspond to any of the static centroids in the centroid database, or in the dynamic centroid database 1460. If it does not correspond to any of the dynamic centroids, the position information is put into a T (NoIP, TLL) group.

In certain embodiments, the centroid module 1420 determines whether any of the location information in the T (IP, TLL) group is actually available even if the derived LL is not found in the dynamic centroid database 1460 or the IP region database 1450. Are determined to include derived LLs, and new dynamic centroids corresponding to these possible derived LLs are generated (1540). For example, if the first number of requests made within a certain period of time with the same IP and the same LL (or LLs within a very close range of each other) is abnormally large, then this same LL or close LL will Since mobile users are unlikely to be in the same place in such a short period of time, they can actually be derived LLs on IP addresses. Centroid module 1420 can check the POI database to determine if the IP address is associated with a POI that accommodates many mobile users. Otherwise, the centroid module 1420 can use these LLs to derive a dynamic centroid (1540) and store the LL with the IP address in the dynamic centroid database 1460. The IP region system 1400 also determines the first number of requests having this IP address and the same LL (or a close LL) (first
number of request) from the T (IP, TLL) group and fetch them from the T (IP, DLL_Dynami
c) Put in a group.

As another example, if there is no IP and the number of second requests made in the same LL (or close LL) within a certain period of time is abnormally large, this same LL (or close LL) It is actually a derived LL because many mobile users are unlikely to be in the same LL in such a short period of time. The centroid module 1420 regards this LL (or a close LL) as a dynamic centroid,
This LL can be stored in the dynamic centroid database 1460. Also, the grouping module 1410 has a second LL with no IP address and the same LL (or a closely spaced LL).
May be taken out from the T (NoIP, TLL) group and put into the (NoIP, DLL_Dynamic) group.

IP region generation module for each IP address in the T (IP, TLL) group
1440 creates an IP region using the TLL associated with this IP address in the T (IP, TLL) group (1550). For example, as shown in FIG. 16, a facility 1600 (eg, a city library)
Using the TLL1601 associated with the IP address of the WiFi device, it is used to derive an IP region 1610, which is a central region derived from the range defined by the TLL1601 and the T (IP, TLL) group. The point is a polygon of centroid 1611. An IP region can be represented by the following set of points.
IP Region = (P1, P2,…, Pm)
Point Pm is represented by the following equation.
Pm = (Lattitudem, Longitudem)
Here, point 1611 is also stored as a centroid associated with IP region 1610. By expressing the region as a set of points, the resolution of the region can be set to an arbitrary level according to the score. For example, a region with three points may be used to encode a triangular region, four points may be used to encode a rectangular region, and so on.

Thus, an IP region is generated from an advertisement request that includes an IP address along with a GPS-based LL. Dynamic LL centroids and dynamic IP centroids are part of a system for tracking and eliminating bad LLs, and therefore are not used to build IP regions. In certain embodiments, certain true LLs are not used to derive a dynamic LL centroid.
For example, if LL occurs at a specific frequency only during the day and not at night, it is not considered to be for dynamic LL centroid derivation. This is because the LL of the router may be a valid POI like a library from which it can be obtained. However, if the LL occurs more than a certain frequency during the night when the user is unlikely to actually be present, it is assumed that it is a derived LL and is eligible to use dynamic LL centroid derivation.

In a particular embodiment, as shown in FIG. 17, if a facility such as an airport 1700 is large, an IP region 1710 derived from a TLL 1701 having a centroid 1711 spans the entire facility linked to the same IP address. Cannot be represented. Because the obtained TLL is concentrated in a small area,
Alternatively, when deriving the centroid 1711 and the IP region 1710, the TLL 1702 is excluded as an abnormal value. Therefore, the IP region engine consults the POI database to see if the calculated IP region is smaller than the POI region stored in the POI database, and if so, places the POI region in the IP region database. Is stored as the IP region of the IP address.

In certain other embodiments, the IP region may be as large as a zip code when the associated IP address corresponds to a cellular IP address of a cellular tower. Therefore, the IP range can be as small as less than 50 meters,
It may be large enough to cover a wide range.

The IP region system 1400 stores the IP generated by the IP region generation module 1440.
Store the region in the database 1450. FIG. 18 shows some examples of IP regions stored in the database 1450 as spatial indexes along with other information such as associated IP addresses and their respective centroids. When an ad request arrives that contains an IP address but does not contain a true LL, the IP region database 1 using the IP address
According to 450, if a match is found, the centroid of the IP region may be used as the estimated location for the ad request, or the entire IP region may be used as the estimated area of the mobile device associated with the ad request. obtain.

FIG. 19 is an illustration of an ad server system 1900 provided by a computer / server system 120 according to a particular embodiment. As shown in FIG. 19, when the processor 202 of the computer / server system 120 executes the advertisement distribution software program 1901 loaded in the main memory 204, the matching module 1920, the ranking module 1920, the one or more advertisement distribution modules An ad server system 1900 is provided that includes either or both the 1930 and an advertising trading interface. The system 1900 utilizes a plurality of databases that store data used and / or generated by the ad server software program 1901 and an advertising campaign database 1950 that stores advertising campaign parameters and advertising documents for distribution to mobile devices. , A database 1960 for storing mobile user intent profiles, a database 1970 for storing historical / statistical data, and a retargeting database (retargetin).
g database) 1980. Some or all of these databases are stored on storage 210
Another server / computer 120 and / or NAS located on or on network 100
121 can be arranged. The process 202 can access the network 100 via the network interface device 208. The advertisement server system further discloses the conventional advertisement server function in addition to the novel features.

In certain embodiments, the return on investment (ROI)
Devices associated with mobile users who visit your brand or store to improve
The ID is stored in the retargeting database 1980. Retargeting database
1980 is executed when a subsequent advertisement request relating to the same or similar mobile user is processed. If the request is from a user whose device ID is found in the retargeting database, an advertising document associated with the brand or store can be selected for delivery to the user. The advertisement may be an advertisement for a particular store so that the mobile user is invited to return to the particular store. Or, if a competitor in the same category of a particular store or any store is in the vicinity of any mobile user, the ad will attract mobile users from competitor stores or other stores in the same category of a particular store May be an advertisement. In this way, more relevant ads are provided to mobile users and the ROI of the advertising campaign is increased. In certain embodiments, referring to FIG. 20A, the retargeting database 198
0 includes, for each brand or store named for retargeting as part of an advertising campaign, a plurality of mobile user identifiers associated with mobile users who visited the brand or store. The timestamp of each visit can also be included so that old data can be discarded or ignored. The retargeting factor is calculated on an event-by-event basis and decays over time, so that older events become less important.

To build a retargeting database, you need to know the specific store / brand where your ad campaign is running and / or other stores / brands in the same category as the specific store (
Mobile device ID (or hash version of the same) associated with the mobile user who visited the store / brand of interest. Document request is an ad request processing system
The mobile device that is processed by the 700 and associated
Is in one or more of the brands (ie, location-based events)
The associated mobile device ID is stored with one or more stores / brands and / or one or more categories associated therewith whenever they find that they are or near. The POI database can be consulted to determine one or more store categories of the store of interest. FIG. 20B shows an example of a location based event showing some requests indicating that an associated mobile user event is taking place at stores B1, B2, and B3. If store B1 is a store of interest, along with store B1 and / or one or more of its associated categories, event number 2,
The device IDs associated with 3,5, ..., and 9975 are stored in the retargeting database. As shown in FIG. 20B, two or more stores are linked to the same user or device ID, indicating that the user is visiting various stores. Also, two or more categories can be associated with one store.

Time stamps in the event database are important because events associated with the same mobile user that occur within the predetermined time range at the same location only mean one long-term visit. Also, if the duration lasts several hours in a series of consecutive days, the related events may simply mean that the mobile user is an employee rather than a customer in a particular business, and these events Are excluded and do not contribute to entries in the retargeting database. A retargeting factor may be calculated for each location-based event based on, for example, the fence type of the fence that was triggered and / or other factors. As shown in FIG. 20B, a retargeting factor may be calculated for each location-based event based on, for example, the fence type of the triggered fence and / or other factors. The retargeting factor of the BR fence type is smaller than the retargeting factor of the BP fence type,
The retargeting factor of the BP fence type is smaller than the retargeting factor of the BC fence type. For example, International Mobile Equipment without privacy
Instead of an Identity (IMEI) number, Apple IDFA and / or Google Advertising ID can be used as a user ID.

Database 1960 stores mobile user intent profiles for a plurality of mobile users. In certain embodiments, each mobile user intent profile is created from location-based events associated with the mobile device carried by each mobile user. Location-based events provide a list of points of interest (POI) that each mobile user has visited over the course of a week or month. These user intent profiles are used as tools for mobile advertisers to re-tune their campaigns and target based on user behavior. Generating the user intent profile is not necessarily consuming real-time computing power, since it is performed after a certain period of time or after a certain amount of location-based events are stored in a database.

The user intent profile is derived from location-based events collected over a period of time, as shown in FIG. 20B. Also, events associated with the same mobile user that occur within the same location and time range represent a single long-term visit, so the timestamp in the event database is important. For several hours in a row, related events may mean that the mobile user is an employee rather than a customer in a particular business. These events may be excluded when the user intent profile is derived.

To derive a specific user's user intent profile, most or all of the events associated with the mobile user (or his / her device ID) are examined and from which the specific user's intent profile is derived. . The intent profile may include, for example, the categories of stores / businesses visited by a particular mobile user, and the number of events in each category. The intent profile can also weight each visit. For example, prolonged stays in business may mean increased interest in mobile users and should be considered in the user intent profile. Also, since older data may be less important than newer data, an attenuation coefficient of 0 <w <1 can be added to the event based on each time stamp.

In certain embodiments, the intent profile database 1960 stores the key as UID or U
It is a derivation / hash value of an ID, and the value can be built on a key-value store such as Redis, which is the intent model data of this UID. One example implementation of an intent model is an interest weight or affiliation wei map that associates an intent score for each category or brand name.
ghts map). The intent score can be updated with the following time decay function.
new_score = old_score * w + 1
0 <w <1.

At the user level, each user's intent profile can be represented as a vector of intent scores: Intent_score = (s_1, s_2, ..., s_n) where s_i is the ith category and / or brand Represents the corresponding intent score.

In certain embodiments, users are grouped into segments based on their intent profile in database 1960. The grouping process is performed based on a vector of intent scores using a clustering algorithm such as the Kmeans algorithm. Once user segments are defined, ad serving is determined individually for each segment.

In certain embodiments, the ad server system 1900 is the same computer / server system 120 that provides the ad server system 1900, or another computer in the network 100.
Receive an advertisement request 910 with relevant information from the request processing system 700, which can be provided by the server system 120. The matching module 1920 compares the characteristics of the request 910 with the relevant information with the requirements of a number of advertisement documents stored in the campaign database to find one or more matching advertisement documents in the campaign database 1950. Or search for multiple matching ads. For example, as shown in FIG. 20C, if each row represents a set of matching criteria for an advertisement document, store Costco may have three sets of matching criteria, each corresponding to a different type of location and / or different trigger accuracy. . They may also belong to different categories of goods / services and require different required attributes, such as different ages of mobile users, different days of the week, and / or different times of the day. An advertiser or seller can offer different prices for requests that meet these different sets of criteria. For example, an advertiser or merchant may charge $ 30 to advertise a request from a user aged 20-50 who triggered a BC location.
It may offer a CPM, while a request from a user of the same age group that triggered the BP location may only offer a $ 10 CPM.

If more than one matching advertising document is found, the ranking module 1920 will determine the type of business with which the advertising document is associated, the price provided to deliver each matching advertising document, the mobile user intent profile in the database,
Historical / statistical and / or retargeting database 1980 in database 1970
Based on the information in, rank the matched advertising documents according to a preset algorithm configured to optimize or improve advertising efficiency. For example, an ad request may trigger both the Costco BR location and the TJ Max BR location. The ranking module 1920 can look up the required attributes and bids for each of these two advertising documents. For example, the mobile user associated with the ad request is a 20-year-old man, and the request is sent on lunch break Monday,
Historical / statistical data suggests that mobile users are more interested in fast food offered by COSCO than shopping at TJ Max because men in their 20s are less likely to visit TJ Max during lunch. Therefore, in such a situation, even though Costco's Ad 01233 in BR location is much lower price than TJ Max's Ad 02457, Ad 01233 can expect a positive reaction to mobile users' advertisements. Selected. On the other hand, if the mobile user associated with the ad request is a 50-year-old woman and the request is sent on Saturday afternoon, it can be inferred that the mobile user is more likely to head to the department store and Ad 0
2457 is selected over Ad 01233.

In certain embodiments, the ranking module 1920 may include a history / statistical data, a mobile user profile, and a Select ad documents from one or more matching ad documents by consulting a retargeting database. Historical / statistical data, mobile user profiles, and / or retargeting databases may be derived from previously fulfilled advertising requests and mobile user responses, as described in further detail below. For example, FIG. 21 lists location information, request time, advertisement category, and mobile user response for each advertisement request associated with a mobile user over a period of six months. From these historical data, it is possible to derive a user intent profile that mobile users tend to respond positively by clicking on ads in the C2 category or visiting stores in the C2 category. Ads in the CI category are almost ignored. Accordingly, the ranking module 1920 prioritizes advertisement documents in the C2 category over advertisement documents in the C1 category. Similarly, sometimes, historical data suggests that users are more likely to respond positively to ads when they are at BP-type locations that cover different business / category parking lots, perhaps while waiting for others. It may indicate something. Such preferences for a particular type of location are also considered by the ranking module 1920 to select an advertising document for distribution to mobile users.

Instead of or in addition to using historical data, use the stats associated with one or more matching ad documents to assist in selecting ad documents for distribution to mobile users. Can be. Statistical data related to the advertising document may be collected from mobile users who actively responded to the same or similar advertising document by visiting the same or similar advertising document store (located at the store's BC / BP) Yes, and those visits are also made a valid response for this purpose. Over time, responses from mobile users can be grouped into different user types, mobile user characteristics such as age, gender, education level, annual income range, and / or device make / model. The distribution across these groups can be used to determine whether current mobile users tend to respond positively to advertising documents. For example, the statistics of advertising documents show that in BR-type locations, women aged 20-40 years with income levels of $ 50-100,000 and college education have a strong tendency to respond positively to certain types of advertising documents It is shown that. Therefore, if the advertisement request 910 includes an attribute that matches the bold attribute in FIG.
If other factors do not otherwise imply, they take precedence.

In certain embodiments, ranking module 1920 employs an algorithm such as method 2300 shown in FIG. Taking into account multiple factors for selecting an advertising document for distribution to the mobile device 130. As shown in FIG. 23, the method 2300 includes, for each matching ad document, a first selection of a matching ad document based on the triggered attributes, the mobile user's age, gender, education level, and other required attributes. Selection factor
Is determined (2310). If a database 1960 is provided and the mobile user's user profile is available, the method includes determining a second selection factor for a matching advertising document based on the user profile or historical data associated with the mobile user. (2320). If a database 1970 is provided and statistics associated with the matching advertisement document are available, the method determines a third selection factor for the matching advertisement document based on the statistics associated with the advertisement document ( 2330). If a database 1980 is provided and contains information related to the advertising document, the method may include retargeting database 1980
Further determining (2340) a fourth selection factor for a matching advertisement document based on information associated with the advertisement document in. Calculating a final selection factor by aggregating the first, second and third selection factors and bids associated with the advertising document (2350). After the final selection factors for all matching advertisement documents have been calculated, the matching advertisement document with the highest selection factor is selected (2360) and delivered to the mobile device associated with the advertisement request 901.

For example, if an ad request related to a 30-year-old male mobile user comes in at lunchtime on weekdays and its relevant information version matches all of the advertising documents shown in FIG. 20C, the category "Fast Food" , The ad documents with the category "
A higher first selection factor may be provided than an advertising document having "electronics." An advertisement document having the category "electronics" can be given a higher first selection factor (SF1) than an advertisement document of the category "general merchandise". An advertisement document “department store” of the category “general product” can be given a higher first selection coefficient than an advertisement document of the category. Here, if the mobile user has a history of preferring electronic advertising, the category "
Advertising documents with "electronics" can be given a higher second selection factor (SF2) than the remaining ads.

In addition, if the statistical results indicate that mobile users are likely to respond to ads in the "electronics" and "general product" categories,
And "General Product" category ad documents have a third selection factor (SF3
) Can be given. In addition, the retargeting database 1980 is consulted to find out if the mobile user has recently entered any of the business locations associated with the advertising document, and a fourth selection factor (SF4) is based on the retargeting information. And given to matching ads. Finally, the first, second, and third selection factors for each ad document are aggregated with the bid value of the ad document by weighted summation, multiplication, or a combination thereof, or other algorithm to generate a final selection factor Yes (FSF).
For example, in one embodiment, as shown in FIG. 24, to calculate the final selection factor, FSF = (
A simple formula of SF1 + SF2 + SF3) * P can be used. P is the bid price of the advertising document.
Thus, in this example, the advertisement 01231 is selected as the advertisement document to send to the requester for delivery to the mobile device 130. In certain embodiments, the advertisement distribution module 1930 retrieves the selected advertisement request from the campaign database 1950, receives the data packet from the advertisement document, and sends the data packet to the requester via the network interface device 208.

In a specific embodiment, as shown in FIG. 25, the ad server system 1900 receives a request 910 with relevant information from the advertisement annotation module 204 and includes one or more computers / servers operating an ad exchange or advertising marketplace. 120 further comprises a marketplace interface module 1940 for transmitting the request with related information 910 in one or more data packets via a packet-based network such as the Internet 110. Advertisement request 910 with relevant information can be provided to ad exchanges for bidding by mobile advertisers via respective computer / server systems 120, each providing a front-end server and a bid calculator and having access to the campaign database. Or posted by the advertising marketplace. The front-end server monitors the advertisement request request posted on the ad exchange and sends the bid generated by the bid calculator. The bid calculator uses conventional or proprietary algorithms, as described above, to calculate the bid price of the ad request posted on the ad exchange and generate a bid for transmission by the front-end server.

In certain embodiments, the marketplace interface module 1940 can determine a minimum bid for a request with related information and attach it to the request with related information 910 before sending it to the bidder. Marketplace interface module 1940 is further configured to receive bids from bidders and / or ad exchanges. Each bid can include information such as bidder ID, request ID, bid price, and the like. The marketplace interface module 1940 forwards the bid received within a preset period to the advertisement supply module after transmitting the advertisement request 910 with the relevant information, which, as described above, Ad requests from bids and / or matching ads from the campaign database based on the price and performance forecast of 9
You can select the advertisement corresponding to 01.

In a specific embodiment, the advertisement request 901 with relevant information is sent to the advertisement ad server system 1.
Filled by another mobile advertiser who has offered the winning bid for ad request 910 instead of 900. The bid calculator of the other mobile advertiser's computer / server system 120 is configured to utilize the information provided in the relevant information request 910 in calculating the bid price. For example, as shown in FIG. 26, in certain embodiments, another mobile advertiser's computer / server 120 is configured to receive a request 910 with relevant information from an ad exchange (2602) and Whether to bid for request 910 is determined by examining the attributes and location in the ad request with relevant information (2604) for a preset campaign criterion similar to that shown in FIG. 20C. For example,
The target store's advertising campaign specifies a location, such as a Walmart building or parking lot, and a request 910 with relevant information includes the business / brand name Walmart and location / type US /
If the location of CA / (Mountain View) / BP is accompanied, a decision to bid is made (
2606). The bid calculator may then use the same or similar processes as described with reference to FIGS. 23 and 24. Generate a bid (2608) (eg, $ 0.15 CPC) for the advertising document that is delivered in response to the advertising request.

In certain embodiments, as shown in FIG.9C, the ad request can attach relevant information to more than one location, and the bid calculator may include more than one of Can be considered. For example, annotate ad requests with business / brand name targets, US / CA / Mountain View / BR and business / brand names Walmart and US / CA / Moun
If you place tain View / BP, the bid calculator will calculate the target ad bid price because the mobile ad is close to two For example, the CPC can be raised to $ 0.20.

Thus, the method and apparatus according to certain embodiments enable a location market. In this market, goods or supplies are mobile requests appropriately tagged with the mobile user's user intent indications, represented by the location where the mobile user is located. Buyers are advertisers who are interested in delivering ads based on location and can bid on location. The marketplace can determine the winning bidder based on bid prices that determine market price efficiency and location-based performance evaluations. Thus, the marketplace can be used to provide maximum benefit to both advertisers and publishers.

FIG. 27 is an illustration of a store visit lift (SVL) system 2700 provided by the computer / server system 120 according to certain embodiments. As shown in FIG.
When executing the SVL software program 2701 loaded in the main memory 204, the processor 202 in the server system 120 executes the group selection module 2710, the advertisement server module 2720, the advertisement request processing module 2730, and the analysis module 2740. The advertisement request processing module system 2700 includes multiple databases that store data used and / or generated by the SVL software program 2701, a database 2750 that stores location-based events, and a database that stores historical / statistical data. 2760, a POI directory 2770, and a database 2780 for map data. Any or all of these databases can be located on storage 210 or on another server / computer 120 and / or NAS 121 in network 100.

FIG. 28 is a flowchart illustrating a method 2800 of increasing a store visit response to a location-based mobile advertisement, according to one embodiment of the present disclosure. The advertisement server 2720 distributes the first digital advertisement document (ad) over the packet-based network to the first group of mobile devices selected by the group selection module 2710 (2810).
. In one embodiment, the group selection module 2710 uses a number of technologies, such as, for example, panel-based, request-based, and software development kit (SDK) to provide mobile user location information, age, gender, education, etc. User information, IMEI (In
mobile device in the form of ternational Mobile Station Station Equipment Identity)
Select a first group of mobile devices for the purpose of collecting mobile device information such as D, mobile device make and model associated with mobile users that may trigger a particular location-based event.

If panel-based technology is used, the first group of mobile devices is associated with a pre-selected panel of mobile users with a given age, gender, education level, income level, and / or model make and model Mobile device. In certain embodiments, a preselected panel of mobile users voluntarily installs an application program (app) on the mobile device and periodically provides its location data to one or more mobile publishers 102 The publisher can share location data with the SVL system 2700 or include location data in requests.

The method 2800 further includes receiving a first set of mobile device data associated with the first group of mobile devices (2820). The first set of mobile devices may come as a result of the mobile publisher sharing location data associated with the user's panel. In certain embodiments, a software development kit (SDK) is provided to the publisher and installed on the publisher. The SDK applies logic to control the timing of location data retrieved from the mobile device. Thus, mobile devices do not need to continuously transmit their location data to maintain battery life. The location data in this panel-based and / or SDK-based approach is typically valid or accurate LL, allowing more accurate determination of the location of the mobile user with respect to the business location of interest.

If a panel of mobile users is not available, the first group of mobile users will be of the general mobile user population whose distribution, such as age, gender, education level, income level, and / or make and model of the mobile device is common. The corresponding distribution can be chosen randomly so that it is representative. The first set of mobile device data is transmitted when interacting with a mobile publisher as part of a request for a document associated with at least some of the first group of mobile devices. The ad request processing module 2730 generates a location or estimated location from these location data for each request, as well as an incoming request, and determines whether the request will trigger any geofences or predefined locations. decide.

The first set of mobile device data is stored in request database 2750. FIG. 30 illustrates an example of a first set of mobile device data according to certain embodiments. The first set of mobile device data may be data received after the first digital advertisement has been transmitted, for example, for a period of 24 hours or 7 days. Group selection module 2710
A second set of mobile device data is identified of the first set of mobile device data including location information indicating a response to the digital advertisement of the second (2830). For example,
As shown in FIG. 30, if the first digital advertisement is to send more traffic to store B1, the group selection module 2710 may send data groups No. 2, 3, 5,.
Selecting from the first set of mobile device data as the second set of mobile device data, the mobile device data being associated within 24 hours of the first digital advertisement being transmitted. This is to indicate that the mobile user visited the store B1.

In some embodiments, the mobile device data stored in the request database does not include a business name, and the analyzer module 2740 may determine that any of the location information in the first set of mobile device data is associated with the first digital advertisement. Determine whether to include geographic coordinates corresponding to one or more geographic regions. For example, if the first digital ad is LL (4
If it is intended to send more traffic to a store located at 5.35,110.75), the group selection module 2710 selects a mobile device data set that includes location coordinates within a range (45.35,110.75). ), As shown in FIG. 30, data group numbers 2, 3, 5,.
, 9975, can be used as a second set of mobile device data.

The analyzer module 2740 also generates statistical results using the second mobile device dataset (2840). Statistical results may include age, gender, educational level, annual income, or other device-level attributes, or one of a set of demographics, such as mobile device make and model, operating system, carrier, time, day of week, etc. One or more related performance trends may be included. As shown in Figure 31, college graduates earn $ 50K to $ 100K per year
Of female mobile users in their 20s to 39 years are more responsive to first digital advertising. These statistical results can then be used by the advertisement server system 1900 to select an advertisement document in response to a subsequent request. For example, if the next request comes from a 27-year-old female mobile user with college education near store B1, the ad server system 1900 will respond to the request with a first digital advertisement for store B1. The precedence is because the statistical results indicate that such mobile users are likely to respond to the advertisement. On the other hand, in the case of a subsequent request from a 35-year-old male mobile user of a high school graduate near the store B1, the advertisement server system 1900 can select a different digital advertisement for another store B2. This is because the statistics show that the mobile user is unlikely to respond to the advertisement of store B1.

In certain embodiments, some or all of the systems 300, 700, 1000, 1400, 1900, and 2700 include one computer / server 120 or multiple computers coupled together via a local and / or wide area network. / Provided by server 120.

Claims (20)

  1. A method performed by a computer system coupled to a packet-based network and processing an advertisement (ad) request,
    Receiving from the packet-based network an advertisement request related to a mobile device;
    Estimating the location of the mobile device based on information in the advertisement request;
    Determining whether the estimated location of the mobile device triggers one or more predefined locations in a geofence database stored in storage;
    Generating an ad request with relevant information that includes one or more triggered locations,
    The method wherein the one or more triggered locations include at least one of a category, a brand name, and a location identifier, and a location represented by a location type.
  2. The method of claim 1, wherein the location type is selected from a group consisting of a business center, a business premises, and a business area.
  3. Searching one or more matching advertisements in the advertisement database that match the request with the related information, selecting an advertisement from the one or more matching advertisements, and transferring the selected advertisements to the packet-based network. The method of claim 1, further comprising transmitting.
  4. The method of claim 1, wherein each matching advertisement of the one or more matching advertisements is associated with a respective location in the request with associated information that matches each of the one or more triggered locations. Item 3. The method according to Item 3.
  5. The advertisement request includes an identifier that identifies the mobile device or mobile user, and selecting an advertisement from the one or more matching advertisements is associated with the mobile user's identifier in a mobile user intent profile database. 4. The method of claim 3, comprising referencing the generated mobile user intent profile.
  6. Selecting an advertisement from the one or more matching advertisements comprises referencing a retargeting database that stores information about mobile users who have visited a geographic location corresponding to one of the triggered locations. 4. The method according to claim 3, comprising:
  7. 4. The method of claim 3, wherein selecting an advertisement from the one or more matching advertisements comprises referencing statistical data associated with at least one of the one or more triggered locations.
  8. The method of claim 1, wherein the request with related information further comprises a price for each of one or more locations, and further comprising sending an advertisement request with related information to the packet-based network.
  9. For one of the one or more triggered locations in the request with the relevant information,
    The method of claim 8, further comprising receiving a bid including a bidder identifier, a request identifier, and a bid price.
  10. Searching the ad database for one or more matching ads that match the relevant information request;
    Selecting an ad from the one or more matching ads;
    Determining whether to accept the bid based on a bid and a price associated with one or more matching ads;
    The method of claim 9, comprising:
  11. Estimating the location of the mobile device comprises:
    Determining whether the advertisement request includes a set of geographic coordinates that satisfy a predefined set of criteria;
    Responsive to a set of geographic coordinates of an advertisement request that does not match a predefined set, determining whether the advertisement request includes an IP address, and querying an IP region database using the IP address;
    In response to finding a matching IP address in the IP region database,
    Using geographic coordinates associated with the matching IP address in an IP region database as an estimated location of the mobile device;
    The method of claim 1, comprising:
  12. The method of claim 11, further comprising: authorizing a first request based on the geographic coordinates associated with the matching IP address.
  13. The method of claim 12, wherein the geographic coordinates are associated with a geographic region, and the confidence is dependent on a size of the geographic region.
  14. A system, coupled to a packet-based network, for processing an ad request associated with a mobile device received from the packet-based network,
    A location module for estimating a location of the mobile device based on information in the advertisement request;
    A geo-fencing module that determines whether the estimated location of the mobile device triggers one or more predefined locations in a geo-fence database stored in storage;
    An annotation module that generates an ad request with relevant information including one or more triggered locations,
    The system wherein the one or more triggered locations are locations represented by at least one of a category, a brand name, and a location identifier, and a location type.
  15. The system of claim 14, wherein the location type is selected from a group consisting of a business center, a business premises, and a business area.
  16. A matching module that searches the advertisement database for one or more matching advertisements that match the request with related information,
    A ranking module that selects an advertisement from the one or more matching advertisements and sends the selected advertisement to the packet-based network;
    15. The system of claim 14, further comprising:
  17. The advertisement request includes an identifier identifying the mobile device or its mobile user, and the ranking module refers to the mobile user intent profile by referring to the mobile user intent profile associated with the identifier in a mobile user intent profile database. 17. The system of claim 16, selecting an advertisement from one or more matching advertisements.
  18. The ranking module selects an advertisement from the one or more matching advertisements by referring to a retargeting database that stores information about mobile users who have visited a geographic location corresponding to one of the triggered locations. 17. The system according to claim 16, wherein:
  19. The ranking module may include at least one of the one or more triggered locations.
    17. The system of claim 16, wherein an advertisement is selected from the one or more matching advertisements by referring to statistics associated with the advertisement.
  20. An advertising marketplace interface that sends the advertisement request with the relevant information to the packet-based network and receives a bid for one of the one or more triggered locations of the request with the relevant information; 15. The system of claim 14, wherein includes a bidder identifier, a request identifier, and a bid price.
JP2019131465A 2014-05-19 2019-07-16 System and method related to mobile advertisement supply on marketing Pending JP2019220193A (en)

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US62/000,501 2014-05-19
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US201462013527P true 2014-06-17 2014-06-17
US62/013,527 2014-06-17
US201462066912P true 2014-10-22 2014-10-22
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